Measuring Feature Dependency of Neural Networks by Collapsing Feature Dimensions in The Data Manifold
- Award ID(s):
- 2205417
- PAR ID:
- 10541978
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-1333-8
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Location:
- Athens, Greece
- Sponsoring Org:
- National Science Foundation
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Graf (2017) warns that every syntactic formalism faces a severe overgeneration problem because of the hidden power of subcategorization. Any constraint definable in monadic second-order logic can be compiled into the category system so that it is indirectly enforced as part of subcategorization. Not only does this kind of feature coding deprive syntactic proposals of their empirical bite, it also undermines computational efforts to limit syntactic formalisms via subregular complexity. This paper presents a subregular solution to feature coding. Instead of features being a cheap resource that comes for free, features must be assigned by a transduction. In particular, category features must be assigned by an input strictly local (ISL) tree-tot-tree transduction, defined here for the first time. The restriction to ISL transductions correctly rules out various deviant category systems.more » « less
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